Voice-Controlled UAV Operation for Public Safety Surveillance
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Solution Overview
Problem
Controlling uncrewed aerial vehicles (UAVs) during public safety incidents is a resource drain for emergency services personnel, diverting their attention from primary tasks and posing safety risks due to the need for manual operation.
Innovation Solution
A communication system integrating radio voice communication devices, UAVs with integrated communication devices, and a processing unit for voice command detection and analytics, enabling automated UAV control through predictive analytics and machine learning to associate voice commands with tasks, allowing for automated UAV operation based on learned commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual control of UAV is implemented, then real-time surveillance capability is improved, but personnel attention and safety are diverted from primary tasks
Solution Approach 1:
The UAV system performs self-control through voice command recognition and automated navigation algorithms. The UAV independently processes surveillance data, identifies targets, and adjusts its flight path without requiring continuous manual intervention, thereby maintaining surveillance capability while freeing personnel for primary tasks
Solution Approach 2:
The patent replaces manual mechanical control with automated voice-based control systems and algorithmic navigation. Voice commands are processed through natural language processing algorithms that translate speech into automated flight control signals, eliminating the need for personnel to manually operate the UAV while maintaining full surveillance functionality
2Ease of operation
If automated control is implemented, then personnel safety and focus on primary tasks are improved, but voice command recognition accuracy may be affected by incident environment
Solution Approach 1:
The system performs preliminary voice training and environmental calibration before deployment. Voice command patterns are pre-registered and the system is trained on incident-specific acoustic environments in advance, allowing the voice recognition algorithm to adapt to background noise and dialect variations, thereby maintaining high recognition accuracy in challenging incident conditions
Solution Approach 2:
The voice recognition system implements continuous feedback loops where recognition confidence levels are monitored in real-time. When confidence thresholds are not met, the system requests clarification or adjusts sensitivity parameters dynamically. This feedback mechanism ensures accurate voice command interpretation even in noisy incident environments while maintaining automated control
3Productivity
If voice command association with tasks is automated, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal voice command framework that handles multiple task types through a single integrated system. The same voice recognition and natural language processing infrastructure supports diverse UAV operations including surveillance, navigation, and data collection, eliminating the need for separate control systems for each function and managing complexity through consolidation
Solution Approach 2:
The system uses template-based voice command structures where common operations are predefined as reusable patterns. Once a voice command is recognized and associated with a task, the association is stored as a template for future use. This copying mechanism allows the system to handle complex task associations efficiently by referencing established patterns rather than processing each command from scratch
Data Source
AI summary
The present specification provides systems, devices and methods for controlling an uncrewed aerial vehicle (UAV) at a public safety incident. An example method contemplates placing a UAV in a shadow mode that follows a firefighter's movements throughout the PSI while monitoring voice activity while the UAV performs tasks such as sending images from a camera to a central server. Potential voice commands are extracted from the voice activity and associated with tasks being performed by the UAV. A machine learning dataset is built from those associations such that at future incidents the UAV can operate in a freelance mode based on detected voice commands or other contextual factors.